System and Method for the Dynamic Generation of Correlation Scores Between Arbitrary Objects

ABSTRACT

Systems and methods are described for performing the dynamic generation of correlation scores between arbitrary objects. When a behavioral event is recorded, that is to say when an end user interacts with multiple objects, relationships between objects are created. These relationships are maintained as a list. When a request for correlated items is requested based upon a seed object, a list of correlated items is dynamically created through the generation of a pivot set and a scoring algorithm to compute the list of correlated items.

This is a non-provisional application is a continuation in partapplication of application Ser. No. 11/369,562 filed Mar. 6, 2006, andbased on provisional application Ser. No. 60/932,718 filed on Jun. 1,2007, and claims priority thereof.

COPYRIGHT AUTHORIZATION

A portion of the disclosure of this patent document may contain materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyrightrights whatsoever.

FIELD OF THE INVENTION

This invention relates generally to creating lists of correlated items.More particularly, the invention relates to systems and methods for thedynamic generation of correlation scores between arbitrary objects tocreate a list of correlated items.

BACKGROUND

Collaborative filtering technology and cluster model technology havecreated processes that are used in the furtherance of business forgenerating sets of items that a consumer might find appealing. Many ofthese processes start by finding a set of customers that have purchasedand rated items. The process chooses items that have interacted withsimilar customers, eliminates items according to business rules andpresents a rank-ordered list of the remaining items to the end user.

Using collaborative filtering technology to generate items of interestor recommendations can become computationally very expensive when thedata sets become large. In an effort to reduce the cost of computation,the data set is generally restricted by reducing the number of usersconsidered, either arbitrarily or by heuristic mechanisms or byrestricting the number of items. The wholesale reduction of the data setmay negatively affect the quality of the items or content to berecommended. In addition, most correlation systems require themaintenance of a series of very large matrices—this can becomputationally costly.

Therefore what has been needed and heretofore unavailable is a systemand method for the dynamic generation of correlation scores betweenarbitrary objects to create a list of correlated items.

SUMMARY

According to one embodiment, a network is disclosed. The networkincludes a plurality of content servers, a client computer to access andestablish relationships with one or more of the content servers and amanagement server to receive relationship information from the serversand to perform collaborative filtering on the relationship informationto generate recommendations for the client.

In a further embodiment, a method is disclosed. The method includesgenerating a seed object, retrieving a list of objects corresponding tothe seed object from a storage table, restricting the list of objectsbased upon a parameter list to generate a pivot set of objects,generating a candidate set by determining objects that have interactedwith each of the pivot set of objects, computing a score for each memberof the candidate set and generating a list of correlated items basedupon at least one score.

BRIEF DESCRIPTION OF THE DRAWINGS

The inventive body of work will be readily understood by referring tothe following detailed description in conjunction with the accompanyingdrawings, in which:

FIG. 1 is a flow chart illustrating one embodiment a technique for thedynamic generation of correlation scores between arbitrary objects forcreating a list of correlated items.

FIGS. 2A-2G illustrate one embodiment of a sequence of events forestablishing a seed list to generate a list of correlated objects.

FIG. 3 is a high-level block diagram of one embodiment of a computingenvironment according to one embodiment of the present invention.

FIG. 4 is a high-level block diagram illustrating one embodiment of afunctional view of a typical computer for use as one of the entitiesillustrated in an environment according to one embodiment of the presentinvention.

FIG. 5 is a high-level block diagram illustrating one embodiment ofmodules within a server according to one embodiment.

FIG. 6 is a high-level block diagram illustrating one embodiment ofmodules active in servicing a customer request according to oneembodiment.

FIG. 7 is a high-level block diagram illustrating one embodiment afunctional view of a technique for increasing recommendation qualityaccording to one embodiment.

FIG. 8 is a high-level block diagram illustrating one embodiment ofmodules within a multi-server operation according to one embodiment.

DETAILED DESCRIPTION

A detailed description of the inventive body of work is provided below.While several embodiments are described, it should be understood thatthe inventive body of work is not limited to any one embodiment, butinstead encompasses numerous alternatives, modifications, andequivalents. In addition, while numerous specific details are set forthin the following description in order to provide a thoroughunderstanding of the inventive body of work, some embodiments can bepracticed without some or all of these details. Moreover, for thepurpose of clarity, certain technical material that is known in therelated art has not been described in detail in order to avoidunnecessarily obscuring the inventive body of work.

Reference in the specification to “one embodiment” or “an embodiment”means that a particular feature, structure, or characteristic describedin connection with the embodiment is included in at least one embodimentof the invention. The appearances of the phrase “in one embodiment” invarious places in the specification are not necessarily all referring tothe same embodiment.

Commonly-assigned U.S. patent application Ser. No. 11/369,562, entitledUsing Cross-Site Relationships to Generate Recommendations filed Mar. 1,2006 (“the '562 application”), which is hereby incorporated by referencein its entirety, describes embodiments of an invention related todetermining recommendations by tracking interactions across multiplewebsites. The present application describes enhancements, extensions,and modifications to, as well as alternative embodiments of, the systemdescribed in the '562 application, as well as new components,architectures, and embodiments. It will thus be appreciated that thematerial described herein can be used in the context of determiningrecommendations by tracking interactions across multiple websites asdescribed in the '562 application, as well as in other contexts.

According to one embodiment, an object and its associated list isdescribed. An object is an entity, item, article or thing for whichinformation can be gathered. Examples of objects include but are notlimited to: a Web page, a person, a collection of lexical descriptors, aword, a shirt, a pair of shoes, an article or anything else capable ofproviding information. Objects are associated together usingclassification information. Examples of classification informationinclude but are not limited to: behavioral data, textual analysis, orany other information that is suitable for associating objects together.Every object is uniquely represented in the system, along with anassociated list of objects that are related to it by various mechanisms,such as behavior, lexical or semantic relationship or any other semanticrepresentation of linkage.

The representation and lists may be stored as a hash table, a balancedbinary tree, or any other mechanism having a suitable medium for storinglike information as is well known to one of ordinary skill in the art.When a unit of classification information is collected, for example, abehavioral event is observed and the list corresponding to each affectedobject is updated. For example, if object A interacts with object B, thelist of A is updated to include B, and the list of B is updated toinclude A. This process may occur through a variety of mechanisms,including offline behavior import, such as dumps of purchase behavior,as well as online implicit behavior gathering of select click streamdata. In addition, other information may be encoded in the list, such asthe type of the object, semantic meaning of the object relationship, thetime of the event, or other classification information.

Embodiments of the invention may include various processes as set forthabove. The processes may be embodied in machine-executable instructions.The instructions can be used to cause a general-purpose orspecial-purpose processor to perform certain steps. Alternatively, theseprocesses may be performed by specific hardware components that containhardwired logic for performing the processes, or by any combination ofprogrammed computer components and custom hardware components.

Elements of the present invention may also be provided as amachine-readable medium for storing the machine-executable instructions.The machine-readable medium may include, but is not limited to, floppydiskettes, optical disks, CD-ROMs, and magneto-optical disks, ROMs,RAMs, EPROMs, EEPROMs, magnetic or optical cards, propagation media orother type of media/machine-readable medium suitable for storingelectronic instructions. For example, the present invention may bedownloaded as a computer program which may be transferred from a remotecomputer (e.g., a server) to a requesting computer (e.g., a client) byway of data signals embodied in a carrier wave or other propagationmedium via a communication link (e.g., a modem or network connection).

FIG. 1 is a flow chart illustrating one embodiment of the dynamicgeneration of correlation scores between arbitrary objects to create alist of correlated items. In processing block 101 a list of correlatedobjects is requested as a result of an external request for recommendedcontent. In processing block 102, a seed object, such as a person or anitem that is being viewed, and a set of parameters are specified, eitherstatically as configuration information or dynamically with the inputrequest, is created.

These parameters may include restrictions (for example, only to considercertain types of behavior) or other options that modify the runtimebehavior of an algorithm. In processing block 103, a list of objects forthe seed object is then retrieved via a lookup of stored relationshipdata in a storage table. The list is then restricted appropriately bywhatever may have been specified in the set of parameters, thusestablishing a pivot set, processing block 104. In processing block 105,each object that has interacted with all members of the pivot set aredetermined by applying appropriate restrictions creating a candidateset.

In processing block 106 as the candidate set is generated, a score foreach member of the pivot set's contribution is computed, and thesescores are summed. In one embodiment, these summed, values include ahistogram that is input to the scoring algorithm. In processing block107, the scored members of the candidate set are then processed by ascoring algorithm, such as the vector cosine or Pearson's R. Atprocessing block 108, the scores are sorted to compute a list ofcorrelated items. In one embodiment, each component of this process iscomputed on a single processor. In another embodiment, processing blocks103, 104, 105, 106, 107 and 108 may each be computed in parallel onmultiple processors or in a distributed fashion across multiple systems.

As discussed above, an entity is something about which information canbe gathered. An entity can be uniquely identified through theassignation of a unique numerical value or through some other process.Entities can be broadly grouped together into types. For example, allpeople might be grouped together into a “people” type. This typeinformation may be encoded into the uniquely identifying value describedabove.

Entities are logically linked by classification events. A classificationevent may be the result of behavioral observations, textual analysis, orsome other means of associating one entity with another. For example, aclassification event can be decomposed into a binary expression A*B,where the * operator implies action. For example, “user U loads Web pageW” could be expressed as U*W, “label L applies to item I” could beexpressed as L*I, or “user U played song S” as U*S. Information aboutentities is stored in a data store.

The data store may be represented through any of several differentmechanisms: a hash table, a balanced tree such as a Red-Black Tree, orsome other data structure. The data store provides the ability, given anentity, to look up all other entities that have been associated with theparticular entity. This information may be stored as an ordered list orsome other data structure. Other information may be stored as well. Forexample, the time might be recorded, the intent that gave rise to theclassification event (e.g., a person purchasing an item), or businesslevel metadata about the transaction (sale status for a product, maturecontent flags for an article, etc.). An association method is used togenerate a list of entities that are related to some other item. Theprocess of association requires at least one initial entity, the seed,and a series of parameters. These parameters may include restrictionsplaced on the members of both the complete pivot set and partialcandidate sets.

FIGS. 2A-2G depict diagrams in accordance with an embodiment of thepresent invention. In FIG. 2A the seed entity “S” 201 is depicted. Seedentity 201 is generated either by the user's interaction with the seeditem, or as the user themselves. The list of entities 202 associatedwith the seed entity 201 is retrieved via a table lookup for allentities with which it is associated, producing the pivot set 203. Eachentity 202 in the pivot set 203 is inspected in turn. If the entity 202in the pivot set 203 does not meet the restriction criteria established(e.g., if purchase information is not being considered and therelationship represents a purchase), then it is excluded from furtherconsideration, and we move on to the next entity 202 in the pivot set203.

In the event that the entity 202 in the pivot set 203 is not excluded,then the list entities 205 associated with this particular entity 202 inthe pivot set 203 is retrieved, producing a partial candidate set 204.Each list entity 205 in the partial candidate set 204 is inspected inturn. The list entity 205 may be excluded from further consideration bythe established restriction criteria (e.g., if the relationshiprepresents a view of an item, and only purchase data is beingconsidered), we move on to the next list entity 205 in the partialcandidate set 204.

Otherwise, the list entity 205 is assigned a raw score. Restrictioncriteria are based on a number of factors, such as the semanticrelationship (intent) associated with the list entity's 205 inclusion inthe partial candidate list 204 or some other criteria. A running sum ofraw scores, each of which represents a weighted relationship between anitem in the pivot set and the candidate set, for each list entity 205 ismaintained, and is not depicted here. Based upon the summation of rawscores and the final scoring algorithm, entities will be generated forserving to a website or end user, not depicted here.

Referring now to FIG. 2B inspection of entity “1” 202A and its partialcandidate set 204A is performed. In FIG. 2B, list entities “a” 205A and“d” 205D fail to meet the criteria for further consideration, and arethus represented in lower case letters. Entity “E” meets the criteria,and is therefore represented in upper case. In FIG. 2C inspection ofentity “2” 202B and its partial candidate set 204B is performed, listentity “a” 205F fails to meet the criteria for further consideration.List entities “B” 205G and “C” 205H meet the criteria. In FIG. 2Dinspection of list entity “3” 202C and its partial candidate set 204C isperformed “a” “b” and “e” represented by 205I, 205J and 205 Lrespectively, do not qualify list entity “D” 205K qualifies.

In FIG. 2E, inspection of entity “4” 202D is performed. List entity “a”205M qualifies and list entity “c” 205N does not. In FIG. 2F no listentity 205 in the candidate set 204E for entity “5” 202E has met thecriteria. Entity “5”, 202E is excluded from the pivot set, and has beendisregarded and its corresponding list entities are never examinedduring candidate set creation.

In FIG. 2G inspection of entity “6” and its partial candidate set 204Fis performed. List entities “a” 205R and “b” 205S do not meet thecriteria, list entities “C” 205T and “D” 205U meet the criteria. The rawscores for each entity, which are computed as a sum of the number ofrelationships between pivot items and candidate items which meet thecriteria, can then be processed by a scoring algorithm, such as thevector cosine or Pearson's R. The results can then be sorted, either bythe output score or some other pre-established criteria, to compute alist of associated entities suitable for input into a display mechanism.Although the foregoing embodiment shows a finite pivot set, a finite setof entities and a finite partial candidate set it should be understoodthat the invention is not so limited and are limited in order tosimplify and clarify the present description.

FIG. 3 is a high-level block diagram of a computing environment 300according to one embodiment of the present invention. FIG. 3 illustratesthree client computers 310A, 310B, 310C, three web sites 312A, 312B,312C, and a server 314 connected by a network 316. At the highest level,end-users of the clients 310 interact with the web sites 312 toestablish relationships. The web sites 312 and/or the clients 310 in oneembodiment describe these relationships to the server 314 in otherembodiments the server determines the relationships. The server 314 usesthe techniques described above in connection with FIGS. 2A-2G and/orother techniques to process correlations in real time to compute listsof highly relevant items.

The client 310 in this embodiment represents a computer that is used byan end-user to interact with the web sites 312 and/or server 314 via thenetwork 316. The client 310 can be, for example, a personal computer oranother network-capable device, such as a personal digital assistant(PDA), a cellular telephone, a pager, a video game system, a television“set-top box” etc. Although FIG. 3 illustrates only three clients 310,embodiments of the present invention can have thousands or millions ofclients participating in the environment 300 described herein. Threeclients 310 are illustrated in order to simplify and clarify the presentdescription.

The web sites 312 are locations on the network 316 that provide webpages to the clients 310 via the network 316. The web sites 312 can be,for example, media sites that primarily provide content such as news tothe end-users, retailer sites that enable the end-users to purchaseitems, social networking sites that enable end-users to interact withother people, and hybrid sites that provide a mix of these features.Those of skill in the art will recognize that there may be an unlimitednumber of different types of web sites 312 with which the clients 310can interact. Although FIG. 3 illustrates only three web sites 312,embodiments of the present invention can have many web sites. Only threewebsites 312 are illustrated in order to simplify and clarify thepresent description. The web sites 312 need not be related or associatedwith each other.

The end-users of the clients 310 interact with the web sites 312 toestablish relationships. For example, assume an end-user views a webpage for a digital camera, and then views a web page for a memory cardfor that camera. These actions create relationships between the end-userand the camera, and between the end-user and the memory card. The websites 312 observe relationships such as these, and provide messages tothe server 314 describing them.

In addition, the web sites 312 receive recommendations from the server314. These recommendations are provided to the end-users, typically byincluding the recommendations on web pages served to the end-users'clients 310. The recommendations can be for arbitrary and/orheterogeneous items and the web sites can request that the server 314provide recommendations for only specified types of items. For example,the recommendations can include items an end-user might want topurchase, news stories the end-user might want to read, bands theend-user might like, discussion groups in which the end-user might wantto participate, etc.

The server 314 receives descriptions of relationships from the web sites312 and/or clients 310 and provides recommendations in return. In oneembodiment, the server 314 performs collaborative filtering on thereceived relationships to generate the recommendations. In otherembodiments, the server 314 performs the method of FIG. 1 to generate atleast one list of items to an end user 310. The relationships can befrom multiple web sites 312 or a single website 312 and/or multipleclients 310 or a single client 310, they can form a large pool of dataon which the recommendations are based. Moreover, in some embodimentsthe relationships created by end-users are tracked across multiple websites 312, meaning that the recommendations are based on a larger set ofrelationships established by that end-user.

The network 316 represents the communication pathways between theclients 310, web sites 312, and server 314. In one embodiment, thenetwork 316 is the Internet. The network 316 can also utilize dedicatedor private communications links that are not necessarily part of theInternet. In one embodiment, the network 316 uses standardcommunications technologies and/or protocols. Thus, the network 316 caninclude links using technologies such as 802.11, integrated servicesdigital network (ISDN), digital subscriber line (DSL), asynchronoustransfer mode (ATM), etc.

Similarly, the networking protocols used on the network 316 can includemulti-protocol label switching (MPLS), the transmission controlprotocol/Internet protocol (TCP/IP), the hypertext transport protocol(HTTP), the simple mail transfer protocol (SMTP), the file transferprotocol (FTP), etc. The data exchanged over the network 316 can berepresented using technologies and/or formats including the hypertextmarkup language (HTML), the extensible markup language (XML), the webservices description language (WSDL), etc. In addition, all or some oflinks can be encrypted using conventional encryption technologies suchas the secure sockets layer (SSL), Secure HTTP and/or virtual privatenetworks (VPNs). In another embodiment, the entities can use customand/or dedicated data communications technologies instead of, or inaddition to, the ones described above.

FIG. 4 is a high-level block diagram illustrating a functional view of atypical computer 400 for use as one of the entities illustrated in theenvironment 300 of FIG. 3 according to one embodiment. Illustrated areat least one processor 402 coupled to a bus 404. Also coupled to the bus404 are a memory 406, a storage device 408, a keyboard 410, a graphicsadapter 412, a pointing device 414, and a network adapter 416. A display418 is coupled to the graphics adapter 412.

The processor 402 may be any general-purpose processor such as an INTELx86, SUN MICROSYSTEMS SPARC, or POWERPC compatible-CPU. The storagedevice 1008 is, in one embodiment, a hard disk drive but can also be anyother device capable of storing data, such as a writeable compact disk(CD) or DVD, or a solid-state memory device. The memory 406 may be, forexample, firmware, read-only memory (ROM), non-volatile random accessmemory (NVRAM), and/or RAM, and holds instructions and data used by theprocessor 402. The pointing device 414 may be a mouse, track ball, orother type of pointing device, and is used in combination with thekeyboard 410 to input data into the computer system 400. The graphicsadapter 412 displays images and other information on the display 418.The network adapter 416 couples the computer system 400 to the network408.

As is known in the art, the computer 400 is adapted to execute computerprogram modules. As used herein, the term “module” refers to computerprogram logic and/or data for providing the specified functionality. Amodule can be implemented in hardware, firmware, and/or software. In oneembodiment, the modules are stored on the storage device 408, loadedinto the memory 406, and executed by the processor 402.

The types of computers 400 utilized by the entities of FIG. 3 can varydepending upon the embodiment and the processing power required for theentity. For example, the client 310 typically requires less processingpower than the web site 312 and server 314. Thus, the client 310 can bea personal computer, cellular telephone, etc. The web site 312 andserver 314, in contrast, may comprise more powerful processors and/ormultiple computers working together to provide the functionalitydescribed herein. In addition, the computers 400 can lack some of thefeatures shown in FIG. 4. For example, a blade server supporting a website 312 may lack a keyboard, pointing device, and display. In oneembodiment, the computer 400 serving as the server 414 utilizes aprocessor 402 and/or memory 406 having a 64-bit word size.

FIG. 5 is a high-level block diagram illustrating modules within theserver 314 according to one embodiment. Those of skill in the art willrecognize that other embodiments can have different and/or other modulesthan the ones described here, and that the functionalities can bedistributed among the modules and/or entities illustrated in FIG. 3 in adifferent mariner.

A communications module 510 communicates with the various web sites 312;clients 310, on the network 316. In one embodiment, the communicationsmodule 510 includes a web server that supports web services and allowsthe server 314 to receive messages describing relationships and/orrequesting recommendations and provide messages containingrecommendations in response.

A normalization module 512 normalizes the messages received from the websites 312 and/or clients 310. In one embodiment, the normalizationmodule 512 analyzes predicates within the messages and verifies that thelabels associated within the predicates are in expected formats. If alabel is not in the expected format, the normalization module 512 altersthe label to place it in the correct format or rejects the message. Thenormalization module 512 modifies labels by adding or removing detailssuch as protocol specifiers (e.g., “http://”), file name extensions(e.g., “.JPEG”), and the like. The normalization process thus ensuresthat like items are consistently identified even if different web sites312, clients 310, and/or remote aggregation modules may use slightlydifferent labels for them.

A canonicalization module 514 canonicalizes the normalized messages fromthe web sites 312 and/or clients 310. In one embodiment, thecanonicalization module 514 associates labels, predicates, and intentswith unique fixed-width integer values. In one embodiment, each uniquelabel is associated with a unique 32-bit value. Each unique predicate istypically represented using fewer than 32 bits because most embodimentshave only a limited number of predicates. Similarly, each unique intentis represented using only a few bits because there are only a limitednumber of possible intent types in one embodiment. In addition, thecanonicalization module 514 maintains a table that associates thelabels, predicates, and intents with their corresponding integers.Canonicalization thus allows each relationship to be stored in a fixedamount of memory.

In one embodiment, the canonicalization module 514 receives normalizedmessages from the normalization module 512 and extracts the labels,predicates and optional intents. The module 514 determines whether thelabels, predicates, and intents have been encountered before and, if so,determines the integers that are associated with them. If a label,predicate or intent has not been encountered before, thecanonicalization module 514 generates an arbitrary and unique integervalue and associates the label, predicate, or intent with it. In oneembodiment, the integer is generated by incrementing thepreviously-generated integer.

In addition, an embodiment of the canonicalization module 514 reversesthe canonicalization process when providing recommendations or in othersituations where it is necessary and/or desired. In one embodiment, theserver 314 generates recommendations internally using the canonicalizedrepresentations of the relationships. The canonicalization module 514maps the canonicalized representations back to their non-canonicalizedmeanings so that the recommendations can be sent out of the server 314.

A relationship storage module 516 stores the canonicalizedrelationships. Further, in one embodiment the storage module 516 storesthe canonicalization table associating labels, predicates, and intentswith their corresponding integer values. In one embodiment, therelationship storage module 516 stores this data in a relationaldatabase, such as a MySQL database.

The relationship storage module 516 also stores the data describing therelationships in a RAM or other relatively fast memory. In oneembodiment, a canonicalized two-tuple, and an optional intent, arestored in a single 64-bit memory word. The relationship storage module516 stores a relationship (i.e., a four-tuple and optional intent) as alinked set of 64-bit words. This is an efficient representation of therelationships and allows for fast manipulation of the relationship databy the computer acting as the server 314.

A recommendation generation module 518 generates recommendations for theweb sites 312 and/or clients 310 based on the relationships stored bythe relationship storage module 516. In one embodiment, therecommendation generation module 518 uses collaborative filtering andoperates in real-time on the relationship data stored in the RAM orother fast memory. Real-time collaborative filtering allows forfiltering based on arbitrary labels, predicates, intents, and/orrelationships. Thus, given a (label, predicate) tuple, stored as a setof entity pairs in the relationship storage module 516, therecommendation generation module 518 generates a set of related (label,predicate) tuples that can be presented as recommendations. If necessaryor desired, the related tuples can be limited to only certain types(e.g., tuples containing only certain predicates or intents). In oneembodiment, the collaborative filtering itself is performed usingconventional techniques.

In one embodiment the collaborative filtering is performed usingrelationships supplied by multiple web sites 312 and/or clients 310, andthe resulting recommendations are of high quality. Moreover, since theactivities of the end-users that provide personally identifiableinformation may be tracked across multiple web sites 312, the pool ofrelationships on which the collaborative filtering is based is deeperthan if only single-site activities were tracked, however, results forsingle website are also contemplated by the invention.

FIG. 6 is a high-level block diagram illustrating modules active inservicing a customer request according to one embodiment. Those of skillin the art will recognize that other embodiments can have differentand/or other modules than the ones described here, and that thefunctionalities can be distributed among the modules and/or entitiesillustrated in FIG. 6 in a different manner.

A user 610 communicates with a customer web site 620, expressing anaction taken on an entity. In this diagram, the user is purchasing anitem, but those of skill in the art will recognize that an embodimentmay encompass many types of interactions with arbitrary entities. As aresult of the interaction with the entity, the customer web site 620needs an output set of recommendations. In one embodiment, the customerwebsite communicates directly with a customer interface module 630directly. In another embodiment, the user 610 directly communicates withthe customer interface module 630 as a result of the response from theweb site 620.

In order to compute the output recommendations, the customer interfacemodule 630 communicates the behavioral interaction data with thecomputation module 640. In one embodiment, the customer interface module630 acts as a protocol translator, encompassing the functionality of thenormalization module 512 and the canonicalization module 514 of FIG. 5to translate the behavior data into an efficient representation forcommunication and storage. In one embodiment, the request to thecomputation module 640 includes behavior data along with a request forrecommendation output in a single message.

In another embodiment, behavior data can be added to the storage module650 in one message, and the request for recommendations can becommunicated as a separate message to the computation module 640. In yetanother embodiment, multiple requests for recommendations can be issuedto the computation module 640 by the customer interface module 630 inresponse to a single request from the user 610 or customer web site 620,using elements of the user's view history to compute multiple sets ofrecommendations which are then returned as a group.

As a result of a request for recommendations with behavior input, thecomputation module 640 adds data points to the storage module 650indicating the entity relationships that were added. In one embodiment,a single entity relationship is added to the storage module 650 as theresult of a single request. In another embodiment, two explicit entityrelationships are added to the storage module 650 as the result of asingle request. In other embodiments, more explicit entity relationshipsare added depending on the circumstances.

In order to compute the recommendations, the computation module 640 thenrequests a vector dump from the storage module 650 for a seed item. Inone embodiment, the seed item is selected by using the input item as theseed item. In another embodiment, the seed item is selected by using theperson making the request as the seed item. The vector of all entitiesassociated with the seed item is then filtered by the computation module640 to create a set of entities that comprise the pivot set. In oneembodiment, the filter conditions for creating the pivot set aresupplied dynamically with the request for recommendations. In anotherembodiment, the filter conditions are supplied statically asconfiguration parameters to the computation module 640.

Once the pivot set is established, the computation module 640 queriesthe list of entities related to each entity in the pivot set bycommunicating with the storage module 650. The list of entities relatedto every member of the pivot set is then put in a table by thecomputation module 640, which comprises the candidate set of recommendeditems. In one embodiment, the candidate set comprises a histogram whereeach occurrence of an entity in the candidate set increments thehistogram count for the entity. In another embodiment, the candidate setcomprises a table of weighted scores, with each entity having a distinctweighted contribution to the score value based on its attributes, suchas the time the relationship was established or other criteria.

Once the candidate set is established, the computation module 640applies a scoring algorithm, such as the vector cosine or Pearson's R,which establishes the output score for the entity. Once the output scoreset is established, the entities are sorted by the output score andreturned to the customer interface module 630. In one embodiment, if theoutput results from the scored set are people, then a secondaryalgorithm is used to select items to which the people are related forreturn to the customer interface module 630. In one embodiment, aselection algorithm for items when the candidate set contains referencesto people would be choosing recently viewed items. In other embodiments,different item selection mechanisms can be employed.

The customer interface module 630 then prepares the results in anexternal form for return to the customer web site 620. In oneembodiment, the customer interface module 630 translates an efficientstored format of the results to an external representation, such asURLs. In another embodiment, the customer interface module 630translates the stored format of the results obtained from thecomputation engine 640 into a set of textual descriptions or image URLs.

After the customer interface module 630 translates the results to anexternal format, they are transmitted to the customer web site 620 forinclusion in the output content returned to the user 610. In oneembodiment, the results are returned to the customer web site 620 andprocessed to include it in the result returned to the user 610. Inanother embodiment, the results are returned directly to the user 610 bythe customer interface module 640.

FIG. 7 is a high-level block diagram illustrating a functional view of amethod for recommendation quality according to one embodiment. Those ofskill in the art will recognize that other embodiments may havedifferent and/or other modules than the ones described here, and thatthe functionalities can be distributed among the modules and/or entitiesillustrated in FIG. 7 in a different manner.

A seed item is selected externally by the computation module 640 (notshown) and the vector of all related entities is fetched for the seeditem in processing block 710. Restrictions are applied to the entitiesin the seed item vector to establish the pivot set in processing block720. The candidate set is created in processing block 730 by fetchingthe list of all related entities for the members of the pivot set andfiltering appropriately.

After the initial candidate set is created in processing block 730, itis evaluated in processing block 740 to determine if the score valuesare acceptable. In one embodiment, score values are a proxy to strengthof behavioral linkage. After the score values are evaluated inprocessing block 740, if they are not determined to be sufficient,execution moves to processing block 750, in which the entire set ofentities in the candidate set are transformed into seed items.Processing block 750 is a logical analog of processing block 710, withthe difference being that processing block 750 uses the entire candidateset to create a set of seed items. With the vectors established for allseed items, execution proceeds to processing block 720 to filter thepivot set exactly as in the single seed item execution case.

When execution reaches evaluation of the candidate processing block 740,if the candidate scores are found to be acceptable, execution proceedsto the final scoring processing block 760, using any scoring algorithmfamiliar to persons skilled in the art, such as vector cosine orPearson's R.

FIG. 8 is a high-level block diagram illustrating modules within amulti-server operation according to one embodiment. Those of skill inthe art will recognize that other embodiments can have different and/orother modules than the ones described here, and that the functionalitiescan be distributed among the modules and/or entities illustrated in FIG.8 in a different manner.

The embodiment depicted in FIG. 8 illustrates a technique fordistributing the data set stored in the relationship storage module 516of FIG. 5 across multiple servers. In one embodiment, the storagemechanism used by the relationship storage module 516 uses DRAM for highspeed access. In order to maintain performance, this embodimentdistributes functionality of the computation module 640 and the storagemodule 650 across more than one server.

The controller module 820 acts as a central mechanism of control for thealgorithm; responsible for controlling which data is stored on thestorage module 830. In one embodiment, the data distribution mechanismis done using a hash of the entity numeric key value. The controllermodule 820 first establishes a pivot set (step 102, 103 and 104) byselecting a node using the data distribution mechanism to locate thecorrect storage module 830 and then requesting the full contents of thevector from the storage module 830. In one embodiment, the storagemodule 830 contains the full entity list of related entities in a singlestorage module 830. In another embodiment, an entity list for a singleentity may exist on more than one storage module 830.

After the pivot set is established (step 104) in FIG. 1, the controllermodule uses the data distribution mechanism to group entities by thenode(s) on which data for the entities is stored. In one embodiment, thecontroller module 820 requests data for multiple entities from a singlestorage module 830 in a single request. In another embodiment, eachrequest for data about an entity from the controller module 820 to thestorage module 830 is performed in multiple requests. When thecontroller module 820 has collected data from the storage module 830 forall entities, it creates the candidate set as described above inconnection with FIG. 1. In one embodiment, the functionality of creatingthe scores for members of the candidate set is distributed between thecontroller module 820 and the storage module 830.

Once the candidate set has been established, the controller module 820processes the members of the candidate set to apply the final scoringalgorithm.

Although the foregoing has been described in some detail for purposes ofclarity, it will be apparent that certain changes and modifications maybe made within the scope of the appended claims. It should be noted thatthere are many alternative ways of implementing both the processes andapparatuses described herein. Accordingly, the present embodiments areto be considered as illustrative and not restrictive, and the inventivebody of work is not to be limited to the details given herein, but maybe modified within the scope and equivalents of the appended claims.

1. A method of generating a correlation score between arbitrary objects,comprising: generating a seed object; retrieving a list of objectscorresponding to the seed object from a storage table; restricting thelist of objects based upon a parameter list to generate a pivot set ofobjects; generating a candidate set by determining objects that haveinteracted with each of the pivot set of objects; computing a score foreach member of the candidate set; and generating a list of correlateditems based upon at least one score.
 2. The method of claim 1, furthercomprising: receiving an external request for recommended content priorto generating the seed object; and retrieving a list of objects relatedto the content.
 3. The method of claim 1, wherein the seed object isgenerated via user interaction with the object.
 4. The method of claim1, wherein generating the list of correlated items comprises sorting thescore for each member using a pre-established criteria.
 5. The method ofclaim 1, wherein the score is calculated using a vector cosinealgorithm.
 6. The method of claim 1, wherein the score is calculatedusing a Pearson's R algorithm. 7.-15. (canceled)
 16. The article ofmanufacture of claim 15, wherein the data, when accessed, results in amachine performing further operations comprising: receiving an externalrequest for recommended content prior to generating the seed object; andretrieving a list of objects related to the content.
 17. The article ofmanufacture of claim 15, wherein the seed object is generated via userinteraction with the object.
 18. The article of manufacture of claim 15,wherein generating the list of correlated items comprises sorting thescore for each member using a pre-established criteria.
 19. The articleof manufacture of claim 15, wherein the score is calculated using avector cosine algorithm.
 20. The article of manufacture of claim 15,wherein the score is calculated using a Pearson's R algorithm. 21.-26.(canceled)